Visualization, modeling and validation of chromatin interaction data

染色质相互作用数据的可视化、建模和验证

• 批准号: 10318167
• 负责人: Feng Yue
• 金额: $ 39.5万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318167
• 关键词: 3-Dimensional; Address; CRISPR/Cas technology; Cell Line; Cell physiology; Cells; ChIP-seq; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Chromatin Remodeling Factor; Chromosome Territory; Communities; Complex; Computer Models; Computing Methodologies; Country; Coupled; Data; Data Analyses; Distal; Elements; Environment; Event; Frequencies; Gene Expression; Gene Expression Regulation; Genes; Genome; Genome engineering; Genomic Segment; Genomics; Hi-C; Intuition; Knock-out; Learning; Link; Machine Learning; Measures; Mediating; Methods; Modeling; Molecular; Procedures; Regulator Genes; Regulatory Element; Research; Resolution; Statistical Models; Structure; System; Techniques; Technology; Tissues; Validation; Visit; Visualization; base; cell type; chromosome conformation capture; convolutional neural network; cost; epigenome; epigenomics; experimental study; genome annotation; genome browser; genome-wide; genomic locus; high throughput technology; histone modification; human embryonic stem cell; interest; mammalian genome; performance tests; predictive modeling; prototype; random forest; repository; transcription factor; transcriptome sequencing; web site

The three dimensional (3D) organization of mammalian genomes is tightly linked to gene regulation, as it can reveal the physical interactions between distal regulatory elements and their target genes. Several recent high- throughput technologies based on Chromatin Conformation Capture (3C) have emerged (such as 4C, 5C, Hi-C and ChIA-PET) and given us an unprecedented opportunity to study the higher-order genome organization. Among them, Hi-C technology is of particular interest due to its unbiased genome-wide coverage that can measure chromatin interaction intensities between any two given genomic loci. However, Hi-C data analysis and interpretation are still in the early stages. One of the main challenges is how to efficiently visualize chromatin interaction data, so that the scientific community to visualize and use it for their own research. In addition, due to the complex experimental procedure and high sequencing cost, Hi-C has only been performed in a limited number of cell/tissue types. Finally, the underlying mechanism of chromatin interactions remains largely unclear. Therefore, the PI will propose the following aims: Aim 1. Build an interactive and customizable 3D genome browser. We will build an interactive and customizable 3D browser, which allows users to navigate Hi-C data and other high-throughput chromatin organization data, including ChIA-PET and Capture Hi-C. We have built a prototype of the 3D genome browser (www.3dgenome.org). Our browser will allow users to conveniently browse chromatin interaction data with other data types (such as ChIP-Seq and RNA-Seq) from the genomic region in the same window simultaneously. Our system will also empower the users to create their own session and query their own Hi-C and other epigenomic data. Aim 2. Impute chromatin interaction using other genomic/epigenomic information. We will predict Hi-C interaction frequencies using other available genomic and epigenomic data in the same cell type, such as ChIP-Seq data for histone modifications and transcription factors. We will build our prediction model and then systematically impute Hi-C interaction matrices for all 127 cell types whose epigenomes are available thanks to recent effort by the ENCODE and Roadmap Epigenome projects. Aim 3. Perform validation experiments for computational method in aim 1 and 2. We will perform 20 3C experiments in hESC and GM cell lines, coupled with genome engineering by CRISPR/Cas9, to evaluate Hi-C prediction method in aim 2.

哺乳动物基因组的三维 (3D) 组织与基因调控密切相关，因为它可以 揭示远端调控元件与其靶基因之间的物理相互作用。近期几位高 基于染色质构象捕获（3C）的通量技术已经出现（例如4C、5C、Hi-C 和 ChIA-PET），为我们提供了研究高阶基因组组织的前所未有的机会。 其中，Hi-C 技术特别受关注，因为它具有无偏差的全基因组覆盖范围，可以 测量任意两个给定基因组位点之间的染色质相互作用强度。 然而，Hi-C 数据分析和解释仍处于早期阶段。主要挑战之一是如何 有效地可视化染色质相互作用数据，以便科学界将其可视化并用于 他们自己的研究。另外，由于实验流程复杂、测序成本较高，Hi-C 仅在有限数量的细胞/组织类型中进行过。最后，底层机制 染色质相互作用在很大程度上仍不清楚。因此，PI将提出以下目标： 目标 1. 构建一个交互式且可定制的 3D 基因组浏览器。我们将建立一个互动的、 可定制的 3D 浏览器，允许用户浏览 Hi-C 数据和其他高通量染色质 组织数据，包括 ChIA-PET 和 Capture Hi-C。我们已经构建了 3D 基因组浏览器的原型 （www.3dgenome.org）。我们的浏览器将允许用户方便地浏览染色质相互作用数据 同一窗口中基因组区域的其他数据类型（例如 ChIP-Seq 和 RNA-Seq） 同时地。我们的系统还将允许用户创建自己的会话并查询自己的 Hi-C 和其他表观基因组数据。目标 2. 使用其他基因组/表观基因组估算染色质相互作用 信息。我们将使用其他可用的基因组和表观基因组数据预测 Hi-C 相互作用频率 在相同的细胞类型中，例如组蛋白修饰和转录因子的 ChIP-Seq 数据。我们将建设 我们的预测模型，然后系统地估算所有 127 种细胞类型的 Hi-C 相互作用矩阵，这些细胞类型 由于 ENCODE 和 Roadmap Epigenome 项目最近的努力，表观基因组变得可用。目标3。 对目标 1 和 2 中的计算方法进行验证实验。我们将进行 20 3C 在 hESC 和 GM 细胞系中进行实验，结合 CRISPR/Cas9 的基因组工程，以评估 Hi-C 目标2中的预测方法。